information sources. Understanding the unique patterns of information sources individuals use may give emergency managers and meteorologists insight into targeting the information they provide. This knowledge may be useful for evaluating information gaps. Better understanding of the demographic and individual differences that explain group/category membership can also help information producers target their messaging strategies. We use concomitant-variable Latent Class Analysis (LCA), also known as latent class regression, to estimate grouped patterns of weather information source reliance and trust. We apply this methodology to data from the Severe Weather and Society Survey, an annual national survey that is conducted by the Center for Risk and Crisis Management at the University of Oklahoma. Preliminary results suggest four distinct use and trust patterns exist among individuals. Interestingly, a majority of individuals can be categorized into either a high engagement, high trust category or lowest engagement with higher trust category. Individual differences including age, emotional response to severe weather, and numeracy help explain category membership. This methodology allows us to simultaneously categorize individuals into different information and trust groups and examine demographic and individual predictors for membership in those groups.